Safe learning is central to AI-enabled robots where a single failure may lead to catastrophic results. Barrier-based method is one of the dominant approaches for safe robot learning. However, this method is not scalable, hard to train, and tends to generate unstable signals under noisy inputs that are challenging to be deployed for robots. To address these challenges, we propose a novel Attention BarrierNet (ABNet) that is scalable to build larger foundational safe models in an incremental manner. Each head of BarrierNet in the ABNet could learn safe robot control policies from different features and focus on specific part of the observation. In this way, we do not need to one-shotly construct a large model for complex tasks, which significantly facilitates the training of the model while ensuring its stable output. Most importantly, we can still formally prove the safety guarantees of the ABNet. We demonstrate the strength of ABNet in 2D robot obstacle avoidance, safe robot manipulation, and vision-based end-to-end autonomous driving, with results showing much better robustness and guarantees over existing models.

我们提出了一种新颖的关注力屏障网络（ABNet），它可以以增量方式构建更大的基础安全模型，通过每个ABNet中的屏障网络头部从不同特征中学习安全的机器人控制策略，并专注于观察的特定部分，从而显著促进了模型的训练同时保证其稳定的输出，并且我们仍然能够正式证明ABNet的安全性保证。我们在2D机器人避障、安全机器人操作和基于视觉的端到端自动驾驶方面展示了ABNet的优势，结果显示出比现有模型更好的鲁棒性和保证性。

ABNet：用于安全且可扩展机器人学习的关注屏障网络